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Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks.

Solanka L, van Rossum MC, Nolan MF - Elife (2015)

Bottom Line: Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity.This beneficial role for noise results from disruption of epileptic-like network states.Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.

View Article: PubMed Central - PubMed

Affiliation: Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom.

ABSTRACT
Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. We show that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation. With moderate noise, variation in excitatory or inhibitory synaptic strength tunes the amplitude and frequency of gamma activity without disrupting grid firing. This beneficial role for noise results from disruption of epileptic-like network states. Thus, moderate noise promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms. Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.

No MeSH data available.


Related in: MedlinePlus

Synaptic weights in scaled and probabilistic variants of the network.(A) Output (top) and input (bottom) synaptic weights of an E (left) and I (right) neuron in the middle of the twisted torus in a network in which synaptic weights are scaled according to the synaptic profile functions from Figure 1B. (B) Same as (A), but synaptic weights are constant and the probability of connection between a pair of neurons is scaled according to the synaptic profile functions in Figure 1B.DOI:http://dx.doi.org/10.7554/eLife.06444.004
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fig1s1: Synaptic weights in scaled and probabilistic variants of the network.(A) Output (top) and input (bottom) synaptic weights of an E (left) and I (right) neuron in the middle of the twisted torus in a network in which synaptic weights are scaled according to the synaptic profile functions from Figure 1B. (B) Same as (A), but synaptic weights are constant and the probability of connection between a pair of neurons is scaled according to the synaptic profile functions in Figure 1B.DOI:http://dx.doi.org/10.7554/eLife.06444.004

Mentions: To systematically explore relationships between strengths of excitatory and inhibitory synapses, computations and gamma activity, we initially take advantage of models that account for both grid firing and theta-nested gamma oscillations through E-I-E interactions (Pastoll et al., 2013). In these models a layer of E cells sends synaptic connections to a layer of I cells, which in turn feedback onto the E cell layer (Figure 1A). For attractor dynamics to emerge the strength of E and I connections are set to depend on the relative locations of neurons in network space (Figure 1B). While suitable connectivity could arise during development through spike timing-dependent synaptic plasticity (Widloski and Fiete, 2014), here the connection profiles are fixed (Pastoll et al., 2013). To vary the strength of excitatory or inhibitory connections in the network as a whole we scale the strength of all connections relative to a maximum conductance value (gE or gI for excitation and inhibition respectively) (Figure 1B). We also consider networks in which the connection probability, rather than its strength, varies according to the relative position of neurons in the network (Figure 1—figure supplement 1). Each E and I cell is implemented as an exponential integrate and fire neuron and so its membrane potential approximates the dynamics of a real neuron, as opposed to models in which synaptic input directly updates a spike rate parameter. Addition of noise to a single E or I cell increases variability in its membrane potential trajectory approximating that seen in vivo (Figure 1C) (Domnisoru et al., 2013; Pastoll et al., 2013; Schmidt-Hieber and Häusser, 2013). Given that all neurons in the model are implemented as exponential integrate-and-fire neurons and that in total the model contains >1.5 million synaptic connections, we optimized a version of the model to enable relatively fast simulation and automated extraction and analysis of generated data (see ‘Materials and methods’). In this way the effect on grid firing of 31 × 31 combinations of gE and gI could be evaluated typically using >50 nodes on a computer cluster in approximately 1 week.10.7554/eLife.06444.003Figure 1.Attractor network model with feedback inhibition and theta frequency inputs.


Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks.

Solanka L, van Rossum MC, Nolan MF - Elife (2015)

Synaptic weights in scaled and probabilistic variants of the network.(A) Output (top) and input (bottom) synaptic weights of an E (left) and I (right) neuron in the middle of the twisted torus in a network in which synaptic weights are scaled according to the synaptic profile functions from Figure 1B. (B) Same as (A), but synaptic weights are constant and the probability of connection between a pair of neurons is scaled according to the synaptic profile functions in Figure 1B.DOI:http://dx.doi.org/10.7554/eLife.06444.004
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4508578&req=5

fig1s1: Synaptic weights in scaled and probabilistic variants of the network.(A) Output (top) and input (bottom) synaptic weights of an E (left) and I (right) neuron in the middle of the twisted torus in a network in which synaptic weights are scaled according to the synaptic profile functions from Figure 1B. (B) Same as (A), but synaptic weights are constant and the probability of connection between a pair of neurons is scaled according to the synaptic profile functions in Figure 1B.DOI:http://dx.doi.org/10.7554/eLife.06444.004
Mentions: To systematically explore relationships between strengths of excitatory and inhibitory synapses, computations and gamma activity, we initially take advantage of models that account for both grid firing and theta-nested gamma oscillations through E-I-E interactions (Pastoll et al., 2013). In these models a layer of E cells sends synaptic connections to a layer of I cells, which in turn feedback onto the E cell layer (Figure 1A). For attractor dynamics to emerge the strength of E and I connections are set to depend on the relative locations of neurons in network space (Figure 1B). While suitable connectivity could arise during development through spike timing-dependent synaptic plasticity (Widloski and Fiete, 2014), here the connection profiles are fixed (Pastoll et al., 2013). To vary the strength of excitatory or inhibitory connections in the network as a whole we scale the strength of all connections relative to a maximum conductance value (gE or gI for excitation and inhibition respectively) (Figure 1B). We also consider networks in which the connection probability, rather than its strength, varies according to the relative position of neurons in the network (Figure 1—figure supplement 1). Each E and I cell is implemented as an exponential integrate and fire neuron and so its membrane potential approximates the dynamics of a real neuron, as opposed to models in which synaptic input directly updates a spike rate parameter. Addition of noise to a single E or I cell increases variability in its membrane potential trajectory approximating that seen in vivo (Figure 1C) (Domnisoru et al., 2013; Pastoll et al., 2013; Schmidt-Hieber and Häusser, 2013). Given that all neurons in the model are implemented as exponential integrate-and-fire neurons and that in total the model contains >1.5 million synaptic connections, we optimized a version of the model to enable relatively fast simulation and automated extraction and analysis of generated data (see ‘Materials and methods’). In this way the effect on grid firing of 31 × 31 combinations of gE and gI could be evaluated typically using >50 nodes on a computer cluster in approximately 1 week.10.7554/eLife.06444.003Figure 1.Attractor network model with feedback inhibition and theta frequency inputs.

Bottom Line: Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity.This beneficial role for noise results from disruption of epileptic-like network states.Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.

View Article: PubMed Central - PubMed

Affiliation: Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom.

ABSTRACT
Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. We show that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation. With moderate noise, variation in excitatory or inhibitory synaptic strength tunes the amplitude and frequency of gamma activity without disrupting grid firing. This beneficial role for noise results from disruption of epileptic-like network states. Thus, moderate noise promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms. Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.

No MeSH data available.


Related in: MedlinePlus